VohoAI/voho-saudi-stt-small

Voho Saudi STT Small

Speech recognition for Saudi Arabic as it is actually spoken: Najdi, Hijazi and Khaleeji dialects, from Voho.

Most Arabic speech models are trained on Modern Standard Arabic: the news, not a phone call from Riyadh. This model is fine-tuned on about 187,000 clips of Saudi dialect speech, and on Saudi speech it makes far fewer mistakes than the model it started from.

Results

Word and character error rate on the Saudi dialect test set (lower is better), before and after fine-tuning:

Dialect	Clips	WER before	WER after	CER before	CER after
All Saudi test clips	4,582	103.9%	39.2%	69.0%	16.9%
Najdi (Riyadh, central)	1,704	102.8%	35.7%	68.7%	15.3%
Hijazi (Jeddah, Makkah)	809	104.3%	36.3%	67.9%	15.4%
Khaleeji (Eastern Province, Gulf)	1,150	106.6%	43.2%	70.5%	18.3%
Saudi, dialect unlabelled	762	135.8%	51.2%	97.3%	25.1%
Modern Standard Arabic	157	52.6%	33.7%	29.5%	12.6%

Scored after the normalisation standard for Arabic speech recognition: diacritics removed, alef forms unified, ta marbuta and alef maqsura normalised, punctuation removed. Both models were scored identically.

Examples

Dialect	What was said	What the model wrote
Khaleeji	الديرة نت الديرة دوت الديرة نت الديرة نت.	هدير هدير عليك برطنا هدير عليك هدير هدير عليك برطنا هدير عليك
Najdi	يا ليتك يا فيصل تعرف وش إللي أبغاه.	ليلتك يا فيصل تعرف وش اللي أبغى

Usage

from transformers import pipeline

asr = pipeline("automatic-speech-recognition", model="VohoAI/voho-saudi-stt-small")
print(asr("call.wav", generate_kwargs={"language": "arabic", "task": "transcribe"})["text"])

Training

• Base model: openai/whisper-small (244M parameters, MIT)
• Data: SADA, the Saudi Audio Dataset for Arabic published by SDAIA and the National Center for AI, Saudi TV speech with dialect labels. Clips labelled Najdi, Hijazi, Khaleeji, unlabelled Saudi and Modern Standard Arabic were kept. Overlapping multi-speaker clips, non-Saudi dialects and clips over 30 seconds were removed.
• Targets: transcripts with diacritics removed
• Setup: 2 epochs, batch size 32, learning rate 1e-5, bf16, one NVIDIA L4

Licence and intended use

Non-commercial. SADA is licensed CC BY-NC-SA 4.0, so this model is released under the same licence. You may use, share and adapt it for research and non-commercial purposes, with attribution, under the same terms. It is not licensed for commercial use.

For production Saudi Arabic speech recognition and synthesis, including in-Kingdom and on-premise deployment, use the Voho API or the LiveKit plugin.

Limitations

• Trained mostly on television speech. Telephone audio (8 kHz, compressed, noisy) is harder, and accuracy on real calls will be lower than the table above.
• Small model: fast, but less accurate than larger speech models.
• Speaker gender in the training data is skewed male.
• Transcripts are written without diacritics.

Citation

Please cite SADA when using this model:

Saudi Audio Dataset for Arabic (SADA), Saudi Data and AI Authority (SDAIA), 2022.